

'''simple docstring'''






import os
from glob import glob

import imageio
import torch
import torchvision
import wandb
from img_processing import custom_to_pil, loop_post_process, preprocess, preprocess_vqgan
from loaders import load_vqgan
from PIL import Image
from torch import nn

from transformers import CLIPModel, CLIPTokenizerFast
from utils import get_device, get_timestamp, show_pil






class      lowercase_      :




  """simple docstring"""




  def __init__( self  :	str   ,lowercase__  :	str = "cpu"   ,lowercase__  :	str = "openai/clip-vit-large-patch14"     ):
   __lowercase				    =					device
   __lowercase				    =					CLIPTokenizerFast.from_pretrained(lowercase__     )
   __lowercase				    =					[0.4_8_1_4_5_4_6_6, 0.4_5_7_8_2_7_5, 0.4_0_8_2_1_0_7_3]
   __lowercase				    =					[0.2_6_8_6_2_9_5_4, 0.2_6_1_3_0_2_5_8, 0.2_7_5_7_7_7_1_1]
   __lowercase				    =					torchvision.transforms.Normalize(self.image_mean   ,self.image_std     )
   __lowercase				    =					torchvision.transforms.Resize(2_2_4     )
   __lowercase				    =					torchvision.transforms.CenterCrop(2_2_4     )






  def    SCREAMING_SNAKE_CASE ( self  :	Optional[Any]   ,lowercase__  :	Dict     ):
   __lowercase				    =					self.resize(lowercase__     )
   __lowercase				    =					self.center_crop(lowercase__     )
   __lowercase				    =					self.normalize(lowercase__     )
   return images






  def __call__( self  :	List[Any]   ,lowercase__  :	str=None   ,lowercase__  :	Any=None   ,**lowercase__  :	List[Any]     ):
   __lowercase				    =					self.tokenizer(text=lowercase__   ,**lowercase__     )
   __lowercase				    =					self.preprocess_img(lowercase__     )
   __lowercase				    =					{key: value.to(self.device     ) for (key, value) in encoding.items()}
   return encoding






class      lowercase_      (nn.Module ):




  """simple docstring"""




  def __init__( self  :	Any   ,lowercase__  :	Optional[int]=1_0   ,lowercase__  :	Optional[Any]=0.0_1   ,lowercase__  :	Optional[int]=None   ,lowercase__  :	Optional[Any]=None   ,lowercase__  :	str=None   ,lowercase__  :	Any=None   ,lowercase__  :	Optional[Any]=None   ,lowercase__  :	Union[str, Any]=None   ,lowercase__  :	Union[str, Any]=False   ,lowercase__  :	Optional[int]=True   ,lowercase__  :	Optional[Any]="image"   ,lowercase__  :	Tuple=True   ,lowercase__  :	Any=False   ,lowercase__  :	Optional[int]=False   ,lowercase__  :	Optional[int]=False   ,):
   super().__init__()
   __lowercase				    =					None
   __lowercase				    =					device if device else get_device()
   if vqgan:
    __lowercase				    =					vqgan
   else:
    __lowercase				    =					load_vqgan(self.device   ,conf_path=lowercase__   ,ckpt_path=lowercase__     )
   self.vqgan.eval()
   if clip:
    __lowercase				    =					clip
   else:
    __lowercase				    =					CLIPModel.from_pretrained('''openai/clip-vit-base-patch32'''     )
   self.clip.to(self.device     )
   __lowercase				    =					ProcessorGradientFlow(device=self.device     )

   __lowercase				    =					iterations
   __lowercase				    =					lr
   __lowercase				    =					log
   __lowercase				    =					make_grid
   __lowercase				    =					return_val
   __lowercase				    =					quantize
   __lowercase				    =					self.vqgan.decoder.z_shape






  def    SCREAMING_SNAKE_CASE ( self  :	Any   ,lowercase__  :	List[Any]=None   ,lowercase__  :	Tuple=None   ,lowercase__  :	Dict=5   ,lowercase__  :	Any=True     ):
   __lowercase				    =					[]
   if output_path is None:
    __lowercase				    =					'''./animation.gif'''
   if input_path is None:
    __lowercase				    =					self.save_path
   __lowercase				    =					sorted(glob(input_path + '''/*'''     )     )
   if not len(lowercase__     ):
    raise ValueError(
        '''No images found in save path, aborting (did you pass save_intermediate=True to the generate'''
        ''' function?)'''     )
   if len(lowercase__     ) == 1:
    print('''Only one image found in save path, (did you pass save_intermediate=True to the generate function?)'''     )
   __lowercase				    =					total_duration / len(lowercase__     )
   __lowercase				    =					[frame_duration] * len(lowercase__     )
   if extend_frames:
    __lowercase				    =					1.5
    __lowercase				    =					3
   for file_name in paths:
    if file_name.endswith('''.png'''     ):
     images.append(imageio.imread(lowercase__     )     )
   imageio.mimsave(lowercase__   ,lowercase__   ,duration=lowercase__     )
   print(F"gif saved to {output_path}"     )






  def    SCREAMING_SNAKE_CASE ( self  :	Dict   ,lowercase__  :	List[Any]=None   ,lowercase__  :	str=None     ):
   if not (path or img):
    raise ValueError('''Input either path or tensor'''     )
   if img is not None:
    raise NotImplementedError
   __lowercase				    =					preprocess(Image.open(lowercase__     )   ,target_image_size=2_5_6     ).to(self.device     )
   __lowercase				    =					preprocess_vqgan(lowercase__     )
   __lowercase    ,      *__lowercase				    =					self.vqgan.encode(lowercase__     )
   return z






  def    SCREAMING_SNAKE_CASE ( self  :	Dict   ,lowercase__  :	Dict     ):
   __lowercase				    =					self.latent.detach().requires_grad_()
   __lowercase				    =					base_latent + transform_vector
   if self.quantize:
    __lowercase    ,      *__lowercase				    =					self.vqgan.quantize(lowercase__     )
   else:
    __lowercase				    =					trans_latent
   return self.vqgan.decode(lowercase__     )






  def    SCREAMING_SNAKE_CASE ( self  :	Dict   ,lowercase__  :	str   ,lowercase__  :	List[Any]   ,lowercase__  :	Optional[int]=None     ):
   __lowercase				    =					self.clip_preprocessor(text=lowercase__   ,images=lowercase__   ,return_tensors='''pt'''   ,padding=lowercase__     )
   __lowercase				    =					self.clip(**lowercase__     )
   __lowercase				    =					clip_outputs.logits_per_image
   if weights is not None:
    __lowercase				    =					similarity_logits * weights
   return similarity_logits.sum()






  def    SCREAMING_SNAKE_CASE ( self  :	Any   ,lowercase__  :	Optional[Any]   ,lowercase__  :	Optional[int]   ,lowercase__  :	Optional[int]     ):
   __lowercase				    =					self._get_clip_similarity(pos_prompts['''prompts''']   ,lowercase__   ,weights=(1 / pos_prompts['''weights'''])     )
   if neg_prompts:
    __lowercase				    =					self._get_clip_similarity(neg_prompts['''prompts''']   ,lowercase__   ,weights=neg_prompts['''weights''']     )
   else:
    __lowercase				    =					torch.tensor([1]   ,device=self.device     )
   __lowercase				    =					-torch.log(lowercase__     ) + torch.log(lowercase__     )
   return loss






  def    SCREAMING_SNAKE_CASE ( self  :	Optional[Any]   ,lowercase__  :	Optional[int]   ,lowercase__  :	Any   ,lowercase__  :	str     ):
   __lowercase				    =					torch.randn_like(self.latent   ,requires_grad=lowercase__   ,device=self.device     )
   __lowercase				    =					torch.optim.Adam([vector]   ,lr=self.lr     )

   for i in range(self.iterations     ):
    optim.zero_grad()
    __lowercase				    =					self._add_vector(lowercase__     )
    __lowercase				    =					loop_post_process(lowercase__     )
    __lowercase				    =					self._get_CLIP_loss(lowercase__   ,lowercase__   ,lowercase__     )
    print('''CLIP loss'''   ,lowercase__     )
    if self.log:
     wandb.log({'''CLIP Loss''': clip_loss}     )
    clip_loss.backward(retain_graph=lowercase__     )
    optim.step()
    if self.return_val == "image":
     yield custom_to_pil(transformed_img[0]     )
    else:
     yield vector






  def    SCREAMING_SNAKE_CASE ( self  :	Union[str, Any]   ,lowercase__  :	Tuple   ,lowercase__  :	List[Any]   ,lowercase__  :	Union[str, Any]     ):
   wandb.init(reinit=lowercase__   ,project='''face-editor'''     )
   wandb.config.update({'''Positive Prompts''': positive_prompts}     )
   wandb.config.update({'''Negative Prompts''': negative_prompts}     )
   wandb.config.update({'''lr''': self.lr, '''iterations''': self.iterations}     )
   if image_path:
    __lowercase				    =					Image.open(lowercase__     )
    __lowercase				    =					image.resize((2_5_6, 2_5_6)     )
    wandb.log('''Original Image'''   ,wandb.Image(lowercase__     )     )






  def    SCREAMING_SNAKE_CASE ( self  :	List[str]   ,lowercase__  :	Any     ):
   if not prompts:
    return []
   __lowercase				    =					[]
   __lowercase				    =					[]
   if isinstance(lowercase__   ,lowercase__     ):
    __lowercase				    =					[prompt.strip() for prompt in prompts.split('''|'''     )]
   for prompt in prompts:
    if isinstance(lowercase__   ,(tuple, list)     ):
     __lowercase				    =					prompt[0]
     __lowercase				    =					float(prompt[1]     )
    elif ":" in prompt:
     __lowercase    ,      __lowercase				    =					prompt.split(''':'''     )
     __lowercase				    =					float(lowercase__     )
    else:
     __lowercase				    =					prompt
     __lowercase				    =					1.0
    processed_prompts.append(lowercase__     )
    weights.append(lowercase__     )
   return {
       "prompts": processed_prompts,
       "weights": torch.tensor(lowercase__   ,device=self.device     ),
   }






  def    SCREAMING_SNAKE_CASE ( self  :	Dict   ,lowercase__  :	Any   ,lowercase__  :	List[Any]=None   ,lowercase__  :	Dict=None   ,lowercase__  :	Optional[int]=True   ,lowercase__  :	str=False   ,lowercase__  :	Any=True   ,lowercase__  :	List[str]=True   ,lowercase__  :	int=None   ,):
   if image_path:
    __lowercase				    =					self._get_latent(lowercase__     )
   else:
    __lowercase				    =					torch.randn(self.latent_dim   ,device=self.device     )
   if self.log:
    self._init_logging(lowercase__   ,lowercase__   ,lowercase__     )

   assert pos_prompts, "You must provide at least one positive prompt."
   __lowercase				    =					self.process_prompts(lowercase__     )
   __lowercase				    =					self.process_prompts(lowercase__     )
   if save_final and save_path is None:
    __lowercase				    =					os.path.join('''./outputs/'''   ,'''_'''.join(pos_prompts['''prompts''']     )     )
   if not os.path.exists(lowercase__     ):
    os.makedirs(lowercase__     )
   else:
    __lowercase				    =					save_path + '''_''' + get_timestamp()
    os.makedirs(lowercase__     )
   __lowercase				    =					save_path

   __lowercase				    =					self.vqgan.decode(self.latent     )[0]
   if show_intermediate:
    print('''Original Image'''     )
    show_pil(custom_to_pil(lowercase__     )     )

   __lowercase				    =					loop_post_process(lowercase__     )
   for iter, transformed_img in enumerate(self._optimize_CLIP(lowercase__   ,lowercase__   ,lowercase__     )     ):
    if show_intermediate:
     show_pil(lowercase__     )
    if save_intermediate:
     transformed_img.save(os.path.join(self.save_path   ,F"iter_{iter:03d}.png"     )     )
    if self.log:
     wandb.log({'''Image''': wandb.Image(lowercase__     )}     )
   if show_final:
    show_pil(lowercase__     )
   if save_final:
    transformed_img.save(os.path.join(self.save_path   ,F"iter_{iter:03d}_final.png"     )     )





import argparse
import os

import gluonnlp as nlp
import mxnet as mx
import numpy as np
import torch
from gluonnlp.base import get_home_dir
from gluonnlp.model.bert import BERTEncoder
from gluonnlp.model.utils import _load_vocab
from gluonnlp.vocab import Vocab
from packaging import version
from torch import nn

from transformers import BertConfig, BertForMaskedLM, BertModel, RobertaTokenizer
from transformers.models.bert.modeling_bert import (
    BertIntermediate,
    BertLayer,
    BertOutput,
    BertSelfAttention,
    BertSelfOutput,
)
from transformers.utils import logging


if version.parse(nlp.__version__) != version.parse("0.8.3"):
 raise Exception("requires gluonnlp == 0.8.3")

if version.parse(mx.__version__) != version.parse("1.5.0"):
 raise Exception("requires mxnet == 1.5.0")

logging.set_verbosity_info()
_SCREAMING_SNAKE_CASE     :					int      =       logging.get_logger(__name__)

_SCREAMING_SNAKE_CASE     :					Any      =       "The Nymphenburg Palace is a beautiful palace in Munich!"
def      UpperCAmelCase__		(UpperCamelCase_				,UpperCamelCase_							):



     """simple docstring"""






     snake_case											=							{
         '''attention_cell''': '''multi_head''',
         '''num_layers''': 4,
         '''units''': 10_24,
         '''hidden_size''': 7_68,
         '''max_length''': 5_12,
         '''num_heads''': 8,
         '''scaled''': True,
         '''dropout''': 0.1,
         '''use_residual''': True,
         '''embed_size''': 10_24,
         '''embed_dropout''': 0.1,
         '''word_embed''': None,
         '''layer_norm_eps''': 1e-5,
         '''token_type_vocab_size''': 2,
     }

     snake_case											=							bort_4_8_768_1024_hparams

     # Let's construct the original Bort model here
     # Taken from official BERT implementation, see:
     # https://github.com/alexa/bort/blob/master/bort/bort.py
     snake_case											=							BERTEncoder(
         attention_cell=predefined_args['''attention_cell''']				,num_layers=predefined_args['''num_layers''']				,units=predefined_args['''units''']				,hidden_size=predefined_args['''hidden_size''']				,max_length=predefined_args['''max_length''']				,num_heads=predefined_args['''num_heads''']				,scaled=predefined_args['''scaled''']				,dropout=predefined_args['''dropout''']				,output_attention=UpperCamelCase_				,output_all_encodings=UpperCamelCase_				,use_residual=predefined_args['''use_residual''']				,activation=predefined_args.get('''activation'''				,'''gelu'''							)				,layer_norm_eps=predefined_args.get('''layer_norm_eps'''				,UpperCamelCase_							)				,)

     # Vocab information needs to be fetched first
     # It's the same as RoBERTa, so RobertaTokenizer can be used later
     snake_case											=							'''openwebtext_ccnews_stories_books_cased'''

     # Specify download folder to Gluonnlp's vocab
     snake_case											=							os.path.join(get_home_dir()				,'''models'''							)
     snake_case											=							_load_vocab(UpperCamelCase_				,UpperCamelCase_				,UpperCamelCase_				,cls=UpperCamelCase_							)

     snake_case											=							nlp.model.BERTModel(
         UpperCamelCase_				,len(UpperCamelCase_							)				,units=predefined_args['''units''']				,embed_size=predefined_args['''embed_size''']				,embed_dropout=predefined_args['''embed_dropout''']				,word_embed=predefined_args['''word_embed''']				,use_pooler=UpperCamelCase_				,use_token_type_embed=UpperCamelCase_				,token_type_vocab_size=predefined_args['''token_type_vocab_size''']				,use_classifier=UpperCamelCase_				,use_decoder=UpperCamelCase_				,)

     original_bort.load_parameters(UpperCamelCase_				,cast_dtype=UpperCamelCase_				,ignore_extra=UpperCamelCase_							)
     snake_case											=							original_bort._collect_params_with_prefix()

     # Build our config 🤗
     snake_case											=							{
         '''architectures''': ['''BertForMaskedLM'''],
         '''attention_probs_dropout_prob''': predefined_args['''dropout'''],
         '''hidden_act''': '''gelu''',
         '''hidden_dropout_prob''': predefined_args['''dropout'''],
         '''hidden_size''': predefined_args['''embed_size'''],
         '''initializer_range''': 0.02,
         '''intermediate_size''': predefined_args['''hidden_size'''],
         '''layer_norm_eps''': predefined_args['''layer_norm_eps'''],
         '''max_position_embeddings''': predefined_args['''max_length'''],
         '''model_type''': '''bort''',
         '''num_attention_heads''': predefined_args['''num_heads'''],
         '''num_hidden_layers''': predefined_args['''num_layers'''],
         '''pad_token_id''': 1,  # 2 = BERT, 1 = RoBERTa
         '''type_vocab_size''': 1,  # 2 = BERT, 1 = RoBERTa
         '''vocab_size''': len(UpperCamelCase_							),
     }

     snake_case											=							BertConfig.from_dict(UpperCamelCase_							)
     snake_case											=							BertForMaskedLM(UpperCamelCase_							)
     hf_bort_model.eval()

     # Parameter mapping table (Gluonnlp to Transformers)
     # * denotes layer index
     #
     # | Gluon Parameter                                                | Transformers Parameter
     # | -------------------------------------------------------------- | ----------------------
     # | `encoder.layer_norm.beta`                                      | `bert.embeddings.LayerNorm.bias`
     # | `encoder.layer_norm.gamma`                                     | `bert.embeddings.LayerNorm.weight`
     # | `encoder.position_weight`                                      | `bert.embeddings.position_embeddings.weight`
     # | `word_embed.0.weight`                                          | `bert.embeddings.word_embeddings.weight`
     # | `encoder.transformer_cells.*.attention_cell.proj_key.bias`     | `bert.encoder.layer.*.attention.self.key.bias`
     # | `encoder.transformer_cells.*.attention_cell.proj_key.weight`   | `bert.encoder.layer.*.attention.self.key.weight`
     # | `encoder.transformer_cells.*.attention_cell.proj_query.bias`   | `bert.encoder.layer.*.attention.self.query.bias`
     # | `encoder.transformer_cells.*.attention_cell.proj_query.weight` | `bert.encoder.layer.*.attention.self.query.weight`
     # | `encoder.transformer_cells.*.attention_cell.proj_value.bias`   | `bert.encoder.layer.*.attention.self.value.bias`
     # | `encoder.transformer_cells.*.attention_cell.proj_value.weight` | `bert.encoder.layer.*.attention.self.value.weight`
     # | `encoder.transformer_cells.*.ffn.ffn_2.bias`                   | `bert.encoder.layer.*.attention.output.dense.bias`
     # | `encoder.transformer_cells.*.ffn.ffn_2.weight`                 | `bert.encoder.layer.*.attention.output.dense.weight`
     # | `encoder.transformer_cells.*.layer_norm.beta`                  | `bert.encoder.layer.*.attention.output.LayerNorm.bias`
     # | `encoder.transformer_cells.*.layer_norm.gamma`                 | `bert.encoder.layer.*.attention.output.LayerNorm.weight`
     # | `encoder.transformer_cells.*.ffn.ffn_1.bias`                   | `bert.encoder.layer.*.intermediate.dense.bias`
     # | `encoder.transformer_cells.*.ffn.ffn_1.weight`                 | `bert.encoder.layer.*.intermediate.dense.weight`
     # | `encoder.transformer_cells.*.ffn.layer_norm.beta`              | `bert.encoder.layer.*.output.LayerNorm.bias`
     # | `encoder.transformer_cells.*.ffn.layer_norm.gamma`             | `bert.encoder.layer.*.output.LayerNorm.weight`
     # | `encoder.transformer_cells.*.proj.bias`                        | `bert.encoder.layer.*.output.dense.bias`
     # | `encoder.transformer_cells.*.proj.weight`                      | `bert.encoder.layer.*.output.dense.weight`

     # Helper function to convert MXNET Arrays to PyTorch
     def to_torch(UpperCamelCase_							) -> nn.Parameter:
          return nn.Parameter(torch.FloatTensor(mx_array.data().asnumpy()							)							)

     # Check param shapes and map new HF param back
     def check_and_map_params(UpperCamelCase_				,UpperCamelCase_							):
          snake_case											=							hf_param.shape

          snake_case											=							to_torch(params[gluon_param]							)
          snake_case											=							gluon_param.shape

          assert (
              shape_hf == shape_gluon
          ), F'''The gluon parameter {gluon_param} has shape {shape_gluon}, but expects shape {shape_hf} for Transformers'''

          return gluon_param

     snake_case											=							check_and_map_params(
         hf_bort_model.bert.embeddings.word_embeddings.weight				,'''word_embed.0.weight'''							)
     snake_case											=							check_and_map_params(
         hf_bort_model.bert.embeddings.position_embeddings.weight				,'''encoder.position_weight'''							)
     snake_case											=							check_and_map_params(
         hf_bort_model.bert.embeddings.LayerNorm.bias				,'''encoder.layer_norm.beta'''							)
     snake_case											=							check_and_map_params(
         hf_bort_model.bert.embeddings.LayerNorm.weight				,'''encoder.layer_norm.gamma'''							)

     # Inspired by RoBERTa conversion script, we just zero them out (Bort does not use them)
     snake_case											=							torch.zeros_like(
         hf_bort_model.bert.embeddings.token_type_embeddings.weight.data							)

     for i in range(hf_bort_config.num_hidden_layers							):
          snake_case											=							hf_bort_model.bert.encoder.layer[i]

          # self attention
          snake_case											=							layer.attention.self

          snake_case											=							check_and_map_params(
              self_attn.key.bias.data				,F'''encoder.transformer_cells.{i}.attention_cell.proj_key.bias'''							)

          snake_case											=							check_and_map_params(
              self_attn.key.weight.data				,F'''encoder.transformer_cells.{i}.attention_cell.proj_key.weight'''							)
          snake_case											=							check_and_map_params(
              self_attn.query.bias.data				,F'''encoder.transformer_cells.{i}.attention_cell.proj_query.bias'''							)
          snake_case											=							check_and_map_params(
              self_attn.query.weight.data				,F'''encoder.transformer_cells.{i}.attention_cell.proj_query.weight'''							)
          snake_case											=							check_and_map_params(
              self_attn.value.bias.data				,F'''encoder.transformer_cells.{i}.attention_cell.proj_value.bias'''							)
          snake_case											=							check_and_map_params(
              self_attn.value.weight.data				,F'''encoder.transformer_cells.{i}.attention_cell.proj_value.weight'''							)

          # self attention output
          snake_case											=							layer.attention.output

          snake_case											=							check_and_map_params(
              self_output.dense.bias				,F'''encoder.transformer_cells.{i}.proj.bias'''							)
          snake_case											=							check_and_map_params(
              self_output.dense.weight				,F'''encoder.transformer_cells.{i}.proj.weight'''							)
          snake_case											=							check_and_map_params(
              self_output.LayerNorm.bias				,F'''encoder.transformer_cells.{i}.layer_norm.beta'''							)
          snake_case											=							check_and_map_params(
              self_output.LayerNorm.weight				,F'''encoder.transformer_cells.{i}.layer_norm.gamma'''							)

          # intermediate
          snake_case											=							layer.intermediate

          snake_case											=							check_and_map_params(
              intermediate.dense.bias				,F'''encoder.transformer_cells.{i}.ffn.ffn_1.bias'''							)
          snake_case											=							check_and_map_params(
              intermediate.dense.weight				,F'''encoder.transformer_cells.{i}.ffn.ffn_1.weight'''							)

          # output
          snake_case											=							layer.output

          snake_case											=							check_and_map_params(
              bert_output.dense.bias				,F'''encoder.transformer_cells.{i}.ffn.ffn_2.bias'''							)
          snake_case											=							check_and_map_params(
              bert_output.dense.weight				,F'''encoder.transformer_cells.{i}.ffn.ffn_2.weight'''							)
          snake_case											=							check_and_map_params(
              bert_output.LayerNorm.bias				,F'''encoder.transformer_cells.{i}.ffn.layer_norm.beta'''							)
          snake_case											=							check_and_map_params(
              bert_output.LayerNorm.weight				,F'''encoder.transformer_cells.{i}.ffn.layer_norm.gamma'''							)

     # Save space and energy 🎄
     hf_bort_model.half()

     # Compare output of both models
     snake_case											=							RobertaTokenizer.from_pretrained('''roberta-base'''							)

     snake_case											=							tokenizer.encode_plus(UpperCamelCase_							)['''input_ids''']

     # Get gluon output
     snake_case											=							mx.nd.array([input_ids]							)
     snake_case											=							original_bort(inputs=UpperCamelCase_				,token_types=[]							)

     # Get Transformer output (save and reload model again)
     hf_bort_model.save_pretrained(UpperCamelCase_							)
     snake_case											=							BertModel.from_pretrained(UpperCamelCase_							)
     hf_bort_model.eval()

     snake_case											=							tokenizer.encode_plus(UpperCamelCase_				,return_tensors='''pt'''							)
     snake_case											=							hf_bort_model(**UpperCamelCase_							)[0]

     snake_case											=							output_gluon[0].asnumpy()
     snake_case											=							output_hf[0].detach().numpy()

     snake_case											=							np.max(np.abs(hf_layer - gluon_layer							)							).item()
     snake_case											=							np.allclose(UpperCamelCase_				,UpperCamelCase_				,atol=1e-3							)

     if success:
          print('''✔️ Both model do output the same tensors'''							)
     else:
          print('''❌ Both model do **NOT** output the same tensors'''							)
          print('''Absolute difference is:'''				,UpperCamelCase_							)


if __name__ == "__main__":
 _SCREAMING_SNAKE_CASE     :					List[Any]      =       argparse.ArgumentParser()
 # Required parameters
 parser.add_argument(
     "--bort_checkpoint_path", default=None, type=str, required=True, help="Path the official Bort params file."
 )
 parser.add_argument(
     "--pytorch_dump_folder_path", default=None, type=str, required=True, help="Path to the output PyTorch model."
 )
 _SCREAMING_SNAKE_CASE     :					Optional[int]      =       parser.parse_args()
 convert_bort_checkpoint_to_pytorch(args.bort_checkpoint_path, args.pytorch_dump_folder_path)




